Determination of Optimal Location and Capacity of Detention Facilities

A new framework for distributed storage tanks placement based on a resilience characteristic metric and reduced modelling

In recent years, urban flooding has been a frequent occurrence, and seriously threatens the safety of lives and properties. Rational placement of distributed storage tanks is one of the effective ways to solve urban flooding, addressing stormwater management and rainwater reuse. However, existing optimization methods (such as genetic algorithm (GA) and other evolutionary algorithms) for determining the placement of storage tanks typically have a high computational burden; as such, they can be very time-consuming, and are not conducive to energy saving, carbon reduction and work efficiency improvements. In this study, a new approach and framework based on a resilience characteristic metric (RCM) and reduced modelling requirements are proposed. In this framework, the resilience characteristic metric, which is based on the linear superposition principle of system resilience metadata, is introduced, and a small number of simulations based on a coupling of MATLAB with SWMM are used to obtain the final placement scheme of storage tanks. The framework is demonstrated and verified with two cases in Beijing and Chizhou, China, and compared with a GA. The GA requires 2000 simulations for two cases (considering the placement of 2 and 6 tanks respectively), while the proposed method needs 44 simulations for the Beijing case and 89 simulations for the Chizhou case. The results show that the proposed approach is feasible and effective, and cannot only obtain a relative better placement scheme, but also considerably reduce computational time and energy consumption. It significantly improves the efficiency of determining the placement scheme of storage tanks. This method provides a new approach for the determining better storage tank placement schemes, and is useful for informing device placement in sustainable drainage systems.

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.